Practical MLOps (ebook)

Practical MLOps (ebook) - Opis

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware Spis treści:
Preface
Why We Wrote This Book
How This Book Is Organized
Chapters
Appendixes
Exercise Questions
Discussion Questions
Origin of Chapter Quotes
Conventions Used in This Book
Using Code Examples
OReilly Online Learning
How to Contact Us
Acknowledgments
From Noah
From Alfredo
1. Introduction to MLOps
Rise of the Machine Learning (...) więcej Engineer and MLOps
What Is MLOps?
DevOps and MLOps
An MLOps Hierarchy of Needs
Implementing DevOps
Configuring Continuous Integration with GitHub Actions
DataOps and Data Engineering
Platform Automation
MLOps
DevOps and MLOps combined best practices?
Conclusion
Exercises
Critical Thinking Discussion Questions
2. MLOps Foundations
Bash and the Linux Command Line
Cloud Shell Development Environments
Bash Shell and Commands
List Files
Run Commands
Files and Navigation
Input/Output
Configuration
Writing a Script
Cloud Computing Foundations and
Building Blocks
Getting Started with Cloud Computing
Python Crash Course
Minimalistic Python Tutorial
Math for Programmers Crash Course
Descriptive Statistics and Normal Distributions
Optimization
Machine Learning Key Concepts
Doing Data Science
Build an MLOps Pipeline from Zero
Conclusion
Exercises
Critical Thinking Discussion Questions
3. MLOps for Containers and Edge Devices
Containers
Container Runtime
Creating a Container
Running a Container
Best Practices
Serving a Trained Model Over HTTP
Edge Devices
Coral
Azure Percept
TFHub
Porting Over Non-TPU Models
Containers for Managed ML Systems
Containers in Monetizing MLOps
Build Once, Run Many MLOps Workflow
Conclusion
Exercises
Critical Thinking Discussion Questions
4. Continuous Delivery for
Machine Learning Models
Packaging for ML Models
Infrastructure as Code for Continuous Delivery of
ML Models
Using Cloud Pipelines
Controlled Rollout of Models
Testing Techniques for Model Deployment
Automated checks
Linting
Continuous improvement
Conclusion
Exercises
Critical Thinking Discussion Questions
5. AutoML and KaizenML
AutoML
MLOps Industrial Revolution
Kaizen Versus KaizenML
Feature Stores
Apples Ecosystem
Apples AutoML: Create ML
Apples Core ML Tools
Googles AutoML and Edge Computer Vision
Azures AutoML
AWS AutoML
Open Source AutoML Solutions
Ludwig
FLAML
Model Explainability
Conclusion
Exercises
Critical Thinking Discussion Questions
6. Monitoring and Logging
Observability for Cloud MLOps
Introduction to Logging
Logging in Python
Modifying Log Levels
Logging Different Applications
Monitoring and Observability
Basics of Model Monitoring
Monitoring Drift with AWS SageMaker
Monitoring Drift with Azure ML
Conclusion
Exercises
Critical Thinking Discussion Questions
7. MLOps for AWS
Introduction to AWS
Getting Started with AWS Services
Using the No Code/Low Code AWS Comprehend solution
Using Hugo static S3 websites
Serverless Cookbook
AWS CaaS
Computer vision
MLOps on AWS
MLOps Cookbook on AWS
CLI Tools
Flask Microservice
Containerized Flask microservice
Automatically build container via GitHub Actions and push to GitHub Container Registry
AWS App Runner Flask microservice
AWS Lambda Recipes
AWS Lambda-SAM Local
AWS Lambda-SAM Containerized Deploy
Applying AWS Machine Learning to the Real World
Conclusion
Exercises
Critical Thinking Discussion Questions
8. MLOps for Azure
Azure CLI and Python SDK
Authentication
Service Principal
Authenticating API Services
Compute Instances
Deploying
Registering Models
Versioning Datasets
Deploying Models to a Compute Cluster
Configuring a Cluster
Deploying a Model
Troubleshooting Deployment Issues
Retrieving Logs
Application Insights
Debugging Locally
Azure ML Pipelines
Publishing Pipelines
Azure Machine Learning Designer
ML Lifecycle
Conclusion
Exercises
Critical Thinking Discussion Questions
9. MLOps for GCP
Google Cloud Platform Overview
Continuous Integration and Continuous Delivery
Kubernetes Hello World
Cloud Native Database Choice and Design
DataOps on GCP: Applied Data Engineering
Operationalizing ML Models
Conclusion
Exercises
Critical Thinking Discussion Questions
10. Machine Learning Interoperability
Why Interoperability Is Critical
ONNX: Open Neural Network Exchange
ONNX Model Zoo
Convert PyTorch into ONNX
Create a Generic ONNX Checker
Convert TensorFlow into ONNX
Deploy ONNX to Azure
Apple Core ML
Edge Integration
Conclusion
Exercises
Critical Thinking Discussion Questions
11. Building MLOps Command Line Tools
and Microservices
Python Packaging
The Requirements File
Command Line Tools
Creating a Dataset Linter
Modularizing a Command Line Tool
Microservices
Creating a Serverless Function
Authenticating to Cloud Functions
Building a Cloud-Based CLI
Machine Learning CLI Workflows
Conclusion
Exercises
Critical Thinking Discussion Questions
12. Machine Learning Engineering
and MLOps Case Studies
Unlikely Benefits of Ignorance in Building Machine Learning Models
MLOps Projects at Sqor Sports Social Network
Mechanical Turk Data Labeling
Influencer Rank
Athlete Intelligence (AI Product)
The Perfect Technique Versus the Real World
Critical Challenges in MLOps
Ethical and Unintended Consequences
Lack of Operational Excellence
Focus on Prediction Accuracy Versus the Big Picture
Final Recommendations to Implement MLOps
Data Governance and Cybersecurity
MLOps Design Patterns
Conclusion
Exercises
Critical Thinking Discussion Questions
A. Key Terms
B. Technology Certifications
AWS Certifications
AWS Cloud Practitioner and AWS Solutions Architect
AWS Certified Machine Learning - Specialty
Data Engineering
Exploratory Data Analysis (EDA)
Machine Learning Implementation and Operations (MLOps)
Other Cloud Certifications
Azure Data Scientist and AI Engineer
GCP
SQL-Related Certifications
C. Remote Work
Equipment for Working Remotely
Network
Physical home network
Power management and home networking
Home Work Area
Health and work area
Home workspace virtual studio setup
Location, Location, Location
D. Think Like a VC for Your Career
Pear Revenue Strategy
Passive
Positive
Exponential
Autonomy
Rule of 25%
Notes
E. Building a Technical Portfolio for MLOps
Project: Continuous Delivery of Flask/FastAPI Data Engineering API on a PaaS Platform
Project: Docker and Kubernetes Container Project
Project: Serverless AI Data Engineering Pipeline
Project: Build Edge ML Solution
Project: Build Cloud Native ML Application or API
Getting a Job: Dont Storm the Castle, Walk in the Backdoor
F. Data Science Case Study:
Intermittent Fasting
Notes on Intermittent Fasting, Blood Glucose, and Food
G. Additional Educational Resources
Additional MLOps Critical Thinking Questions
Additional MLOps Educational Materials
Education Disruption
Current State of Higher Education That Will Be Disrupted
10X Better Education
Built-in apprenticeship
Focus on the customer
Lower time to completion
Lower cost
Async and remote first
Inclusion first versus exclusion first
Nonlinear versus serial
Life-long learning: permanent access to content for alumni with continuous upskill path
Regional job market that will be disrupted
Disruption of hiring process
Conclusion
H. Technical Project Management
Project Plan
Weekly Demo
Task Tracking
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